{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basics of Matplotlib\n",
    "\n",
    "### What is Matplotlib\n",
    "The [matplotlib](http://matplotlib.org) library is a powerful tool capable of producing complex publication-quality figures with fine layout control in two and three dimensions. While it is an older library, so many libraries are built on top of it and use its syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The inline flag will use the appropriate backend to make figures appear inline in the notebook.  \n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# `plt` is an alias for the `matplotlib.pyplot` module\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import seaborn library (wrapper of matplotlib)\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load car loan data into a pandas dataframe from a csv file\n",
    "filename = 'data/table_i702t60.csv'\n",
    "df = pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>starting_balance</th>\n",
       "      <th>interest_paid</th>\n",
       "      <th>principal_paid</th>\n",
       "      <th>new_balance</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>car_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>34689.96</td>\n",
       "      <td>202.93</td>\n",
       "      <td>484.30</td>\n",
       "      <td>34205.66</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34205.66</td>\n",
       "      <td>200.10</td>\n",
       "      <td>487.13</td>\n",
       "      <td>33718.53</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33718.53</td>\n",
       "      <td>197.25</td>\n",
       "      <td>489.98</td>\n",
       "      <td>33228.55</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>33228.55</td>\n",
       "      <td>194.38</td>\n",
       "      <td>492.85</td>\n",
       "      <td>32735.70</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32735.70</td>\n",
       "      <td>191.50</td>\n",
       "      <td>495.73</td>\n",
       "      <td>32239.97</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  starting_balance  interest_paid  principal_paid  new_balance  \\\n",
       "0      1          34689.96         202.93          484.30     34205.66   \n",
       "1      2          34205.66         200.10          487.13     33718.53   \n",
       "2      3          33718.53         197.25          489.98     33228.55   \n",
       "3      4          33228.55         194.38          492.85     32735.70   \n",
       "4      5          32735.70         191.50          495.73     32239.97   \n",
       "\n",
       "   interest_rate       car_type  \n",
       "0         0.0702  Toyota Sienna  \n",
       "1         0.0702  Toyota Sienna  \n",
       "2         0.0702  Toyota Sienna  \n",
       "3         0.0702  Toyota Sienna  \n",
       "4         0.0702  Toyota Sienna  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the first 5 rows of the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60 entries, 0 to 59\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   month             60 non-null     int64  \n",
      " 1   starting_balance  60 non-null     float64\n",
      " 2   interest_paid     60 non-null     float64\n",
      " 3   principal_paid    60 non-null     float64\n",
      " 4   new_balance       60 non-null     float64\n",
      " 5   interest_rate     60 non-null     float64\n",
      " 6   car_type          60 non-null     object \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 3.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking to make sure we dont have nans in our dataframe\n",
    "# It is not easy to directly plot data that contains nans\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>starting_balance</th>\n",
       "      <th>interest_paid</th>\n",
       "      <th>principal_paid</th>\n",
       "      <th>new_balance</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>car_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>34689.96</td>\n",
       "      <td>202.93</td>\n",
       "      <td>484.30</td>\n",
       "      <td>34205.66</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34205.66</td>\n",
       "      <td>200.10</td>\n",
       "      <td>487.13</td>\n",
       "      <td>33718.53</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33718.53</td>\n",
       "      <td>197.25</td>\n",
       "      <td>489.98</td>\n",
       "      <td>33228.55</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>33228.55</td>\n",
       "      <td>194.38</td>\n",
       "      <td>492.85</td>\n",
       "      <td>32735.70</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32735.70</td>\n",
       "      <td>191.50</td>\n",
       "      <td>495.73</td>\n",
       "      <td>32239.97</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  starting_balance  interest_paid  principal_paid  new_balance  \\\n",
       "0      1          34689.96         202.93          484.30     34205.66   \n",
       "1      2          34205.66         200.10          487.13     33718.53   \n",
       "2      3          33718.53         197.25          489.98     33228.55   \n",
       "3      4          33228.55         194.38          492.85     32735.70   \n",
       "4      5          32735.70         191.50          495.73     32239.97   \n",
       "\n",
       "   interest_rate       car_type  \n",
       "0         0.0702  Toyota Sienna  \n",
       "1         0.0702  Toyota Sienna  \n",
       "2         0.0702  Toyota Sienna  \n",
       "3         0.0702  Toyota Sienna  \n",
       "4         0.0702  Toyota Sienna  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_number = df.loc[:, 'month'].values\n",
    "interest_paid = df.loc[:, 'interest_paid'].values\n",
    "principal_paid = df.loc[:, 'principal_paid'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "       52, 53, 54, 55, 56, 57, 58, 59, 60])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The values attribute converts a column of values into a numpy array\n",
    "type(month_number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  `plot` method\n",
    "Plotting month_number on the x axis and principal paid on the y axis. As a reminder, if you dont know what a method accepts, you can use the in built-in function `help`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, scalex=True, scaley=True, data=None, **kwargs)\n",
      "    Plot y versus x as lines and/or markers.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "    \n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "    \n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "    \n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "    \n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "    ...      linewidth=2, markersize=12)\n",
      "    \n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "    \n",
      "    \n",
      "    **Plotting labelled data**\n",
      "    \n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "    \n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "    \n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFrame` or a structured numpy array.\n",
      "    \n",
      "    \n",
      "    **Plotting multiple sets of data**\n",
      "    \n",
      "    There are various ways to plot multiple sets of data.\n",
      "    \n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "    \n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "    \n",
      "    - Alternatively, if your data is already a 2d array, you can pass it\n",
      "      directly to *x*, *y*. A separate data set will be drawn for every\n",
      "      column.\n",
      "    \n",
      "      Example: an array ``a`` where the first column represents the *x*\n",
      "      values and the other columns are the *y* columns::\n",
      "    \n",
      "      >>> plot(a[0], a[1:])\n",
      "    \n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "    \n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using\n",
      "    :rc:`axes.prop_cycle`.\n",
      "    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional and default to ``range(len(y))``.\n",
      "    \n",
      "        Commonly, these parameters are 1D arrays.\n",
      "    \n",
      "        They can also be scalars, or two-dimensional (in that case, the\n",
      "        columns represent separate data sets).\n",
      "    \n",
      "        These arguments cannot be passed as keywords.\n",
      "    \n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "    \n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "    \n",
      "        This argument cannot be passed as keyword.\n",
      "    \n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "    \n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n",
      "            could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            ``plot('n', 'o', '', data=obj)``.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    list of `.Line2D`\n",
      "        A list of lines representing the plotted data.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, default: True\n",
      "        These parameters determine if the view limits are adapted to the\n",
      "        data limits. The values are passed on to `autoscale_view`.\n",
      "    \n",
      "    **kwargs : `.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "    \n",
      "        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n",
      "    \n",
      "        If you make multiple lines with one plot call, the kwargs\n",
      "        apply to all those lines.\n",
      "    \n",
      "        Here is a list of available `.Line2D` properties:\n",
      "    \n",
      "        Properties:\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "        alpha: float or None\n",
      "        animated: bool\n",
      "        antialiased or aa: bool\n",
      "        clip_box: `.Bbox`\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        color or c: color\n",
      "        contains: unknown\n",
      "        dash_capstyle: {'butt', 'round', 'projecting'}\n",
      "        dash_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "        data: (2, N) array or two 1D arrays\n",
      "        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "        figure: `.Figure`\n",
      "        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "        linewidth or lw: float\n",
      "        marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n",
      "        markeredgecolor or mec: color\n",
      "        markeredgewidth or mew: float\n",
      "        markerfacecolor or mfc: color\n",
      "        markerfacecoloralt or mfcalt: color\n",
      "        markersize or ms: float\n",
      "        markevery: None or int or (int, int) or slice or List[int] or float or (float, float) or List[bool]\n",
      "        path_effects: `.AbstractPathEffect`\n",
      "        picker: unknown\n",
      "        pickradius: float\n",
      "        rasterized: bool or None\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        solid_capstyle: {'butt', 'round', 'projecting'}\n",
      "        solid_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        transform: `matplotlib.transforms.Transform`\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xdata: 1D array\n",
      "        ydata: 1D array\n",
      "        zorder: float\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of varying size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "    \n",
      "    A format string consists of a part for color, marker and line::\n",
      "    \n",
      "        fmt = '[marker][line][color]'\n",
      "    \n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "    \n",
      "    Other combinations such as ``[color][marker][line]`` are also\n",
      "    supported, but note that their parsing may be ambiguous.\n",
      "    \n",
      "    **Markers**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'.'``          point marker\n",
      "    ``','``          pixel marker\n",
      "    ``'o'``          circle marker\n",
      "    ``'v'``          triangle_down marker\n",
      "    ``'^'``          triangle_up marker\n",
      "    ``'<'``          triangle_left marker\n",
      "    ``'>'``          triangle_right marker\n",
      "    ``'1'``          tri_down marker\n",
      "    ``'2'``          tri_up marker\n",
      "    ``'3'``          tri_left marker\n",
      "    ``'4'``          tri_right marker\n",
      "    ``'s'``          square marker\n",
      "    ``'p'``          pentagon marker\n",
      "    ``'*'``          star marker\n",
      "    ``'h'``          hexagon1 marker\n",
      "    ``'H'``          hexagon2 marker\n",
      "    ``'+'``          plus marker\n",
      "    ``'x'``          x marker\n",
      "    ``'D'``          diamond marker\n",
      "    ``'d'``          thin_diamond marker\n",
      "    ``'|'``          vline marker\n",
      "    ``'_'``          hline marker\n",
      "    =============    ===============================\n",
      "    \n",
      "    **Line Styles**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "    \n",
      "    Example format strings::\n",
      "    \n",
      "        'b'    # blue markers with default shape\n",
      "        'or'   # red circles\n",
      "        '-g'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        '^k:'  # black triangle_up markers connected by a dotted line\n",
      "    \n",
      "    **Colors**\n",
      "    \n",
      "    The supported color abbreviations are the single letter codes\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "    \n",
      "    and the ``'CN'`` colors that index into the default property cycle.\n",
      "    \n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a2207a7d0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Not the prettiest plot\n",
    "plt.plot(month_number, interest_paid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a241c0550>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c/wT4A2Amra3Z3QcAomNT1N4OnEo7ry9qu46ZbTazXjPrHRkZudV+i4jIe7hpuJvZJ4Fhd9+e4c+0Wdr8hgb3Le7e4+49jY2NGf5oERHJRFEG5zwMfMrMPgGUAdVm9lfAkJm1uvuAmbUCw9H5fUBn2uc7gP657LSIiLy3m87c3f05d+9w90WEC6Xfd/dfBV4BnoxOexJ4OXr9CvCEmZWa2WKgG9g25z0XEZF3lcnM/d08D2w1s6eAk8DjAO6+38y2AgeAKeBpd5++456KiEjGzP2Gcvhd19PT4729vbnuhohIXjGz7e7eM9t7ukNVRCSGFO4iIjGkcBcRiSGFu4hIDCncRURiSOEuIhJDCncRkRhSuIuIxJDCXUQkhhTuIiIxpHAXEYkhhbuISAwp3EVEYkjhLiISQwp3EZEYUriLiMSQwl1EJIYU7iIiMaRwFxGJIYW7iEgM3TTczazMzLaZ2W4z229mfxy115vZq2Z2NDrWpX3mOTM7ZmaHzexj2RyAiIjcKJOZ+wTwiLuvBzYAj5rZTwHPAq+5ezfwWvQ9ZrYKeAJYDTwKfNnMCrPQdxEReRc3DXcPLkbfFkdfDjwGvBi1vwh8Onr9GPCSu0+4+3HgGLBpLjstIiLvLaOau5kVmtkuYBh41d1/AjS7+wBAdGyKTm8HTqV9vC9qe+fP3GxmvWbWOzIycgdDEBGRd8oo3N192t03AB3AJjNb8x6n22w/YpafucXde9y9p7GxMaPOiohIZm5ptYy7XwBeJ9TSh8ysFSA6Dken9QGdaR/rAPrvtKMiIpK5TFbLNJpZbfS6HPgIcAh4BXgyOu1J4OXo9SvAE2ZWamaLgW5g2xz3W0RE3kNRBue0Ai9GK14KgK3u/h0z+xGw1cyeAk4CjwO4+34z2wocAKaAp919OjvdFxGR2Zj7DeXwu66np8d7e3tz3Q0RkbxiZtvdvWe293SHqohIDCncRURiSOEuIhJDCncRkRhSuIuIxJDCXUQkhhTuIiIxpHAXEYkhhbuISAwp3EVEYkjhLiISQwp3EZEYUriLiMSQwl1EJIYU7iIiMaRwFxGJIYW7iEgMKdxFRGJI4S4iEkMKdxGRGLppuJtZp5n9wMwOmtl+M/t81F5vZq+a2dHoWJf2mefM7JiZHTazj2VzACIicqOiDM6ZAn7f3XeYWRWw3cxeBX4NeM3dnzezZ4FngWfMbBXwBLAaaAO+Z2bL3H16rjt/6txl/u03drO0qZIljZUsbQpfLdVlmNlc/zoRkbxx03B39wFgIHo9bmYHgXbgMeBD0WkvAq8Dz0TtL7n7BHDczI4Bm4AfzXXnL05MMTk1wyu7+hm7OpVsrywtYkljBUuisF8aBX9X/QKKClWJEpH4y2TmnmRmi4CNwE+A5ij4cfcBM2uKTmsHfpz2sb6o7Z0/azOwGaCrq+uWOw6wsrWab/6rh3F3Ri5OcGz4Im8OX+TY8EWOjVzkh8fO8M0dp5PnlxQWsLihgqXNIfC7m0PoL26ooLSo8Lb6ICIyH2Uc7mZWCfwN8AV3H3uPssdsb/gNDe5bgC0APT09N7x/K8yMpqoymqrK+OklDde9N3b12nWBf2zoIvtOj/K3ewfw6LcWFhj31S9gSVMl3U0h9LubqljSWEl5iUJfRPJPRuFuZsWEYP9rd/9m1DxkZq3RrL0VGI7a+4DOtI93AP1z1eFbVV1WzMauOjZ21V3XfvXaNG+OXEzO9o9GXz84NMzUTEh9M+ioK49m+VUsjcJ/aVMlVWXFuRiOiEhGbhruFqbofwYcdPcX0t56BXgSeD46vpzW/lUze4FwQbUb2DaXnZ4LZcWFrG6rYXVbzXXt16ZnOHHmEsfSAv/o0Dg/fPMsk1MzyfPaaspY2lwVZvpNIfy7myupVuiLyDyQycz9YeBfAHvNbFfU9oeEUN9qZk8BJ4HHAdx9v5ltBQ4QVto8nY2VMtlSXFgQBXUVH09rn5qe4dT5KxwdGudoVOY5MjTOtuNnuXotFfot1WXJWv6yRPg3V1FTrtAXkbvH3O+o3D0nenp6vLe3N9fduC0zM87pC1c4EoX+kaHxMOsfusiVa6n/pjVVlYawbw6hv6w5hL5m+iJyu8xsu7v3zPbeLa2WkRsVFBid9QvorF/Ah1c2J9sToX90eJwjQyH0jw5d5KVtp64L/cRMf3lzVTL8u5urqCzVH42I3D4lSJakh/4jK24M/SNDIfSPDo1zeGic//njt5lIq+m315azrLmSZS1VLGuqYnlLuKBbVqzVOyJycwr3u+zdZvrTM86pc5c5PDTO0aHUbP+Hx84yOR1C3wzuq19Ad3NVmOm3hOPihgpKinRzloikKNznicICY1FDBYsaKvjY6pZk+9T0DCfOXkqGfWLG//1Dw0xHSzaLCozFDRXJsF/WXMWKlio66xdQWKBtGETuRQr3ea6osIClTVUsbariE2tbk+0TU9O8NXIpGfiHBy+yp+8C/2fPQPKcsuICuptSYZ8I/+bqUu29IxJzCvc8VVpUyMrWala2Vl/XfmliKqzaGQy1/MOD4/zj0RH+Zkdf8pya8mKWN4c6/rKWKPi1XFMkVhTuMVNRWsSGzlo2dNZe137u0iSHB8Ms/1B0/PbO04xPpDZca60pY3lLCP0VLVUsb65mSZP23RHJRwr3e0R9RQkPLVnIQ0sWJtvcnf7RqxweHAuBPxiC/4fHznBt+vp6fiLwV7RUs7ylio66cpV2ROYxhfs9zMxory2nvbb8uuWa16ZnOH7mEocGxzk8OMbhwXF2nbrAd9Lq+ZWlRWmBX8WK1hD6uilLZH7QHaqSsfGr1zgyNM7BgVDLPzw4zqHBsev20m+vLY/CPszyV7ZWsWhhhfbRF8kC3aEqc6KqrJgH76vnwfvqk23uzsDoVQ5FpZ1DUfD/w5GR5O6aJUUFdDdVJsN+RUs1K1qraKgszdVQRGJP4S53xMxoqy2n7R2lnYmpaY4NX4xm9+McHBi7YdVOY1UpK1qqolU/IfSXNFbqhiyROaBwl6woLZp9S+WzFyeSYZ84/uUPTyTvwi0uNJY0VrKqNczuE8s9NcsXuTUKd7mrFlaW8vDSUh5emnpiVmIP/QMDidLOGP/05lm+uTP1iMSGylJWtlaxKgr7la3V3N9YQbFq+SKzUrhLzqXvof9YWvv5S5McHBhLhv7BgTH+Im2WX1JYQHdzJStbq5Ohv6q1mpoFWrEjotUykleuTc/w1sglDgyMcnAgBP7BgTHOXJxMntNWU8aqturrQr+rfgEF2mdHYkarZSQ2igsLknfR/uLGVPvw+FUO9I8lA//AwBjfPzRMtGCHipJCVkRhv6otHJe3VGkLZYktzdwltq5em+bI0HgU+iHwDw6MczHacqGwwFjSWJEW+DWsaqumvqIkxz0XyYxm7nJPKisuZF1HLes6apNtMzNO3/krHBgY5UD/GPv7x9h2/Bzf3tWfPKe1puy6Gf7qtho667XdguQXhbvcUwoKjK6FC+hauIBH16S2UD5/aZIDA2Mc6A8z/P39o7x+ZCS5Z35VaVGo4beFr9Vt1XQ3VWlNvsxbNw13M/tz4JPAsLuvidrqga8Di4ATwGfd/Xz03nPAU8A08Hvu/t2s9FxkDtVVlPDw0obrlmhevTbN4cHxZNjv7x/j62+knoGbWK2zuq06WtMfLt5W6Pm3Mg/ctOZuZh8ELgL/Iy3c/xNwzt2fN7NngTp3f8bMVgFfAzYBbcD3gGXuPv0uPx5QzV3yx/SMczxak7+/P1XaOXcprNYxg8ULK1jdHsJ+TRT6darjSxbcUc3d3f/RzBa9o/kx4EPR6xeB14FnovaX3H0COG5mxwhB/6Pb6rnIPFNYYCxtqmRpUyWfWt8GhP11Bseusv90CPr9/aPsePs8/3t3qo7fVlN2feC3V9NSXaY6vmTN7f7/Y7O7DwC4+4CZNUXt7cCP087ri9puYGabgc0AXV1dt9kNkdwzM1prymmtKecjq1L76yTq+PtOjyZD/3sHh0j8z3J9RUkI+/Ya1rTVsKY9rMdX4MtcmOvi4Gz/Vs5a93H3LcAWCGWZOe6HSM7NVse/NDHFocEx9p1Ohf5//8e3kjtoVpUVsbqtmrXtNaxpD3vzLG6o0IPO5ZbdbrgPmVlrNGtvBYaj9j6gM+28DqD/hk+L3KMqSotu2DZ5YmqaI4MX2dc/yt4o8F/80dtMToVtFhaUFLKqNZrht4cZ/tLGSu2RL+/pdsP9FeBJ4Pno+HJa+1fN7AXCBdVuYNuddlIkzkqLClnbUcPajho+F7Vdm57hzZGLyRn+vtOjbO09xV/+04noMwWsbA0z/LXtoYa/rLlKG6lJUiarZb5GuHjaAAwB/wH4NrAV6AJOAo+7+7no/D8CfgOYAr7g7v/3Zp3QahmRmwsrdULg7z0dZvkH+seSd9yWFBWwsqWKNVHgr+2oUeDH3HutltH2AyJ5bGbGOXH2Enuj2f3e06PsPz3G+LsE/pr2Gpa3KPDjQuEucg+ZmXHePnc5Ffh9o+zrH2U8etZtSWEBK1tD4K/rqGFtey3dzZUK/DykcBe5x83MOCfPXWZPFPh7+i5cN8NP1PBD2NewrqOWJY16sPl8p3AXkRskZvh7+i6w7/Qou/tG2X96lEuT4Yby8uLCsCyzIzXDv7+hQvvizyMKdxHJyMyM89aZS+w9fYE9faGks79/LLmfTmVpEWvaq6PdNmtY116rHTNzSFv+ikhGCtK2V/jFjR0ATE3P8ObIJfb0hcDfc3r0uoea1y4oZm17DeujwF/fWUtzdVkuhyFo5i4it2FyaoYjQ+Ps7rvA3r5Q0jkyNJ7cIrm5upS17bWsj8J+XUcNtQu0edpc08xdROZUSVFB8o5Z3h/arkxOc2BglN2nwgXbPafDXjoJ9y1cwLqOVOCvaauhvESPOcwWhbuIzInyksIbtlYYu3qNfX2j7Oq7wJ5To2w/cS65W2ZhgdHdVMmGzvC0rPWduulqLqksIyJ31fD4VfZEs/tdfeF44fI1AMqKC1jdFur36zvD8b6F2inz3Wi1jIjMW+5hDf7uvlF2n7rA7lMX2Nc/ytVrqQu2Iexr2RAF/sLK0hz3en5QzV1E5i0z476FFdy3sCL5AJSp6RmODF1kVxT2u/su8KffP0p0vZbO+nI2dNaxvqOGDZ21rGmvoaxY9ft0mrmLSF64NDHF3tOj7Dp1IZR0Tl6gf/QqAEUFxvKWKjZ01rKhs5aNXbXc31AZ+xuuVJYRkVgaHrvK7r5Rdp06H0L/1GhyS4Wq0iLWddZEgV/Hhs5aGqviVc5RWUZEYqmpuoyPrirjo9HjDcMdthfZefICu06Fr//6D28l19+315azsSs1u1/dFt9yjsJdRGIj3GFbxdKmKh7vCQ+FuzI5zb7+UXZFgb/z5AW+s2cAgOJCY2VrNRs7a9nYVcfGrtrYPMdWZRkRuecMj11l56lE2J9nT98ol6MN0+orSsLMPgr89Z01VJUV57jHs1NZRkQkTVN1GR9b3cLHVrcA4SlXR4bGo3LOeXacvMD3D4VHQ5tBd1MlGzvDzH5jVx3dTfP/Yq1m7iIisxi9co09faGMs+PkeXaevMDolXCzVVVpEes7a3mgK1XOycXeOZq5i4jcopryYj7Q3cgHuhuBcLPV8TOXrgv7P/3BseTa+/sbKtjYVccD99XyQFcdy5qrKMzh7F4zdxGR23RpYoo9faNR2IdyzrlLk0DY+359Zw0PdNXxQJZm9zmZuZvZo8CXgELgK+7+fLZ+l4hILlSUFvHQkoU8tGQhkNpKYcfJ8+x4O8zwv/z6m8mlmPc3VvBgVx0P3BcCP5u1+6zM3M2sEDgCfBToA94APufuB2Y7XzN3EYmrd87ut799nvPRRmlVZUX8ck8n/+6Tq27rZ+di5r4JOObub0UdeAl4DJg13EVE4mq22f2Js5fZ8fZ5tp88T2tteVZ+b7bCvR04lfZ9H8kt/QMz2wxsBujq6spSN0RE5hczY3FDBYsbKvilBzuy9nuytSv+bEWk6+o/7r7F3XvcvaexsTFL3RARuTdlK9z7gM607zuA/iz9LhEReYdshfsbQLeZLTazEuAJ4JUs/S4REXmHrNTc3X3KzH4H+C5hKeSfu/v+bPwuERG5UdbWubv73wJ/m62fLyIi706PGRcRiSGFu4hIDCncRURiaF5sHGZmI8DbGZzaAJzJcnfuJo1n/orTWCBe44nTWODOxnOfu896o9C8CPdMmVnvu+2jkI80nvkrTmOBeI0nTmOB7I1HZRkRkRhSuIuIxFC+hfuWXHdgjmk881ecxgLxGk+cxgJZGk9e1dxFRCQz+TZzFxGRDCjcRURiKG/C3cweNbPDZnbMzJ7NdX9ulZn9uZkNm9m+tLZ6M3vVzI5Gx7pc9jFTZtZpZj8ws4Nmtt/MPh+15+t4ysxsm5ntjsbzx1F7Xo4HwqMuzWynmX0n+j6fx3LCzPaa2S4z643a8nI8ZlZrZt8ws0PR35+HsjWWvAj36Jms/xn4OLAK+JyZ3d5DB3PnL4FH39H2LPCau3cDr0Xf54Mp4PfdfSXwU8DT0Z9Hvo5nAnjE3dcDG4BHzeynyN/xAHweOJj2fT6PBeBn3X1D2nrwfB3Pl4C/c/cVwHrCn1F2xuLu8/4LeAj4btr3zwHP5bpftzGORcC+tO8PA63R61bgcK77eJvjepnwMPS8Hw+wANhBeCxkXo6H8HCc14BHgO9EbXk5lqi/J4CGd7Tl3XiAauA40UKWbI8lL2buzP5M1vYc9WUuNbv7AEB0bMpxf26ZmS0CNgI/IY/HE5UxdgHDwKvuns/j+RPgD4CZtLZ8HQuER3T+vZltj569DPk5nvuBEeAvopLZV8ysgiyNJV/C/abPZJW7z8wqgb8BvuDuY7nuz51w92l330CY9W4yszU57tJtMbNPAsPuvj3XfZlDD7v7A4Sy7NNm9sFcd+g2FQEPAP/F3TcCl8hiOSlfwj2uz2QdMrNWgOg4nOP+ZMzMignB/tfu/s2oOW/Hk+DuF4DXCddH8nE8DwOfMrMTwEvAI2b2V+TnWABw9/7oOAx8C9hEfo6nD+iL/q8Q4BuEsM/KWPIl3OP6TNZXgCej108SatfznpkZ8GfAQXd/Ie2tfB1Po5nVRq/LgY8Ah8jD8bj7c+7e4e6LCH9Pvu/uv0oejgXAzCrMrCrxGvg5YB95OB53HwROmdnyqOnDwAGyNZZcX2S4hYsRnwCOAG8Cf5Tr/txG/78GDADXCP8FfwpYSLjwdTQ61ue6nxmO5WcIZbE9wK7o6xN5PJ51wM5oPPuAfx+15+V40sb1IVIXVPNyLIQ69e7oa3/i734ej2cD0Bv9u/ZtoC5bY9H2AyIiMZQvZRkREbkFCncRkRhSuIuIxJDCXUQkhhTuIiIxpHAXEYkhhbuISAz9f/JIf/9eRHvxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can also plot another line on the same graph\n",
    "plt.plot(month_number, interest_paid)\n",
    "plt.plot(month_number, principal_paid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose Figure Style\n",
    "We will use `plt.style.available` to select an appropriate aesthetic styles for our figures. \n",
    "\n",
    "The default style is not the most aesthetically pleasing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our style choices\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a242fb0d0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SQxI1efBkPTXxKUUFRsndzd3ZTQU6jWZPYv7oRz/S888/r3Hjxik8PFzbtm2TJE2YMEHu7u7q16+f1q1bp/nz56ugoEAZGRlau3at/f2zZs1SWlqaRowYocDAQG3atEmRkZEsfAXQKTTYGoxpmb+PhjROy0T0j1BiSKIei3lMiSGJGuo3lGkZoA2aHUxuv/12VVRU6PXXX9elS5c0cuRIvfjii/LyMlaKr1mzRqmpqVq4cKH8/f21ZMkS++iIJM2YMUPl5eVav369/YC15cuXt/8dAUA7+ObqNzp49qA9hGQVZ+ly3WXFD4zXhIETtOHODUoYmCA/D7/v/zAAzeaSmZnZupWpN1F1dbVmzpypyspK1pgA6BBnrWe1r3iffVompzRHt3rdqsSQRE0ImaDEkESNCxinnm49nd1UwLSsVqt8fHy0fft2+8BFS7EfDUC3U9dQp8/Of2ZfoLq3eK/OWM8ockCkEkMStWT8Ek0ImaDBPoOZlgE6GMEEQJdXXlOu/Wf2GyMiZ/Yp+0y23FzdlDAwQRNCJmhB1ALFBcfpll63OLupQLdHMAHQpdhsNh3/+rh9WmbfmX0quFCgMP8wTQiZoPtH368Nd27QqH6j5Obq5uzmAvgWggmATq3xSPesM1naV7xP+8/sV1VtlWKDYzVh4AStnrZaCSEJPFsG6CQIJgA6DZvNplMVp+whJOtMlvJK8xR4S6AmhEzQ7UNv1y8n/1LjAsZxdgjQSRFMAJhWzdUaHS45rKziLHsY+brma0UFRGlCyAT9PPHnShiYoBCfkO//MACdAsEEgGkUVxYr60yWsoqztO/MPuWU5MjPw08TQiYoYWCCliYslSXQIo+eHs5uKoCbhGACwCku113WpyWf2kdD9p/Zr9KqUkUOiFTCwAQtjlusCSETFOobypZdoBshmADoENeOhmSdyVJOaY68e3krYWCCEgYm6PG4xxUTFKM+7n2c3VQATkQwAdDuvms0pKSqxD4a8m+x/6aEkAQN8xvGaAiAJggmANrEZrOpqLLIHkCyzmQppyRHvr19lRCSoPHB4xkNAdBsBBMALfLN1W906NwhewjZf2a/LlRf0LiAcRo/cLwWxy1WwsAEnrILoFUIJgBuyGaz6cuLX2r/mf3KPpOt/Wf3K680T/29+hvHuQ+coKXjl8oSZJFnT09nNxdAF0AwAWBXcblCB84e0P4z+40wcjZb1bXVig6M1viB4/XzxJ8rPjheg3wGMRoC4KYgmADdVF1DnY6WHTVCyFkjiHzx1Rca4jtECSEJuivsLq2askpjA8ZyiiqADkMwAbqJkkslyj6bbR8NOXTukFxdXBUXHKf44Hj99vbfKn5gPM+UAeBUBBOgC/rm6jf6tORT+7qQ7DPZOmM9o4j+EUoYmKAHIx9U2ow0nrALwHQIJkAn12Br0PGvjyv7TLZ9ROTI+SPq59lP8QPjNT54vBbFLFJMUIy8e3k7u7kA8A8RTIBO5kL1BWWfzbYHkQNnD+hy3WVZgiyKD47XzxN/rvEDx7NAFUCnRDABTKzmao1ySnN04OwBexg5WXFSI/qOUHxwvP5p5D9p9fTVihwQyQJVAF0CwQQwiQZbg778+ssmoyF55/Pk29tX8cHxig+O10NjH1JccJz8PPyc3VwAuCkIJoCTnK86b5+KOXD2gA6eO6jLdZcVHRituKA4LZ+wXPHB8RriO4QpGQDdBsEE6ABVtVX6tORT+5TMgbMHVFxZrFH9RykuOE7/HP7PWnv7Wo25dQxTMgC6NYIJ0M4aDy67NoQcvXBUAX0CFB8cr7jgOC2KWSRLoEU+vX2c3VwAMBWCCdAGNptNpypO2adiss9m6/C5w+rh2kOxwbGKC4rTqimrFBccp2DvYGc3FwBMj2ACtMCF6gs6eO5gk3UhFZcrNHbAWMUGxWpB1AK9MvMVjew7koPLAKAVCCbADVy7LqQxjJyqOKWRfUcqNjhWd4XdpV9N/pXGBoxV7x69nd1cAOgSCCaApNr6Wn12/jN7ADl47qDyL+QrsE+gfUrm0ehHFRMUI9/evs5uLgB0WQQTdDsNtgZ98dUXOnjuoA6ePagD5w4orzRPHj09FBsUq9igWP166q8VGxyroFuCnN1cAOhWCCbo0mw2m4oqi+wh5OC5gzp07pDqGuoUFRil2KBYPRn/pGKDYjXMf5hcXVyd3WQA6NYIJuhSSqtKdejcoSYh5GLNRd024DbFBsXqp7f9VC8lv6TRt45WD1d+/AHAbPiXGZ1WeU25Dp07ZASRc0YQOWs9q5H9Rio2yFic+svJv9TYAWPl0dPD2c0FADQDwQSdwqUrl5RTmqODZw/qUIkxInKi/IRCfUMVExSj8QPHa3HcYlmCLPLu5e3s5gIAWolgAtP55uo3yivNM0ZDSowRkYILBQq8JVCxQbGKCYrRQ2MfkiXIon6e/ZzdXABAOyKYwKmu1F3RZ2Wf2deFHCo5pKNlR+Xv4a+YoBjFBMXoR6N+JEuQhR0yANANEEzQYa7WX9XRC0ft60IOnTukI+ePqI97H1mCLIoNitWvJv9KMUExCvEO4Ym6ANANEUxwU9Q11KngQoEOnTukwyWHdejcIeWW5qpXj16yBFoUExSjFYkrFBMUo1DfUEIIAEASwQTtoL6hXse+OmYPII0hxNXFVdGB0bIEWrQ4brFig2MV5h/GWSEAgBsimKBF6hvq9cXXX+jwucNNRkJssikqIEqWQIsWxSxSTFCMRvQdwYPsAAAtQjDBDX07hBwuOayckhw12Bo0LmCcYoJi9Gj0o7IEWRTeL5wDywAAbcZvEkhqOh3TGERyS3PtIcQSaNGCqAWKuTuGEAIAuGn47dINNS5M/bTkU/tISG5priTZQwgjIQAAZ+A3Thd3tf6q8i/kNwkheaV5cnVxVVRglKIDorXQslCWQCOEsCYEAOBMBJMupLa+Vp+XfW6EkHOH9Wnppzpy/oh6uPZQdGC0ogOi9W+x/yZLoIWFqQAAU2pWMHnzzTf11ltvNalLTEzUr3/9a0lScXGxUlNTlZ+fLz8/P82bN08zZsxocv2WLVu0bds2VVVVyWKxaNmyZfL392+n2+h+Ltdd1pHzR/Rpyaf20ZDPzn8mz56eRggJjNaS+CWKDozW8L7D2aILAOgUmj1iEh4ert/85jf2r93d3SVJdXV1SklJUVhYmDZv3qz8/HylpqZqwIABslgskqQdO3bo7bffVkpKioKCgpSWlqZVq1Zpw4YN7Xw7XVNVbZXySvOMEFJqBJGjZUfl09vHfk7ILxJ/oejAaA31G8phZQCATqvZwaRHjx7fOcKRnZ2tsrIyvfrqq/L09FRoaKjy8vKUnp5uDybp6emaPXu2Jk2aJElasWKF5syZo8LCQoWFhbXTrXQN5TXlyi3NbRJCvvjqC93qdassQRZFB0TrnhH3KDowWoN8BhFCAABdSrODyYkTJ3TvvffKy8tLFotFCxYs0C233KJjx44pPDxcnp6e9mujo6P12muvSZJqa2t14sQJLVy40P56UFCQAgIClJ+f362DSVl1mX0qprGcrDipQT6DFB0YraiAKD0w+gEeYAcA6DaaFUwiIiKUkpKi4OBglZaW6rXXXtPTTz+t9evXq7y8XL6+vk2u9/X1VUVFhSTJarWqoaFBfn5+N7ymq7PZbCqqLFJOaY4+LfnU/ue5S+c03H+4ogOjFRccp8diHlNUQJT6evZ1dpMBAHCKZgWTuLg4+38PHTpUgwcP1oMPPqjjx49/73ttNlurG7dy5Ur7Wpbk5GQlJye3+rM6Sn1Dvb68+KVySnKUU5pjDyGVlysV0T9C0YHRumPoHVoxYYXGBoyVdy9vZzcZAIBW27lzp3bu3CnJmCVpq1ZtFw4ODlafPn1UUlIiPz8/FRUVNXm9oqLCPori4+MjV1dXlZeX3/CaG1m9erW8vc37i7u2vlZHy47aR0FySnOUV5qnelu9IgdEKiogSvdF3KfV01brtgG3qXeP3s5uMgAA7eragQOr1aqNGze26fNaFUzOnz+vqqoqBQQEqGfPntq6datqamrk4eEhScrJydGoUaMkGbt3hg0bptzcXPti2JKSEpWWlioiIqJNje9Il65cUt75vCYjIUfLjsqzp6eiAqMUFRClxyyPKSowitNSAQBopWb99ty8ebMSExPVv39/lZSUaPPmzRo9erRGjBih+vp69evXT+vWrdP8+fNVUFCgjIwMrV271v7+WbNmKS0tTSNGjFBgYKA2bdqkyMhI0y58PV913ggf14SQwouFCuwTaA8hdw+/W1GBUQr1DWVnDAAA7aRZweT8+fN65plnZLVa1bdvX8XGxmrBggVydXWVq6ur1qxZo9TUVC1cuFD+/v5asmSJfXREkmbMmKHy8nKtX7/efsDa8uXLb9pNNZfNZtPfyv/WJITkluaqpKpEYf5higowQsgjUY8oKiBKA/oMcHaTAQDo0lwyMzNbvzr1JqmurtbMmTNVWVnZbmtMautrlX8hX7mlucopyVHu+Vzlluaq5mqNRt862h5CogKjFDkgkkWpAAC0kNVqlY+Pj7Zv3y4vL69WfUaXXAhReblSeefzjBDy91GQo2VH5dHTQ+MCxmncgHF6aOxDirozShH9I+Tu5u7sJgMAAHXyYGKz2VRsLVZuaW6TcrLipIJvCVZUYJTGDRinH474ocYFjNMQ3yE8MwYAABPrlMFk9+nd+tXHv1Juaa6sV6wK7xeucQHjNCFkgn4W+zONCxinfp79nN1MAADQQp0ymAT0CdBPb/upfnvHbzW6/2h59PRwdpMAAEA76JTBZHjf4Rred7izmwEAANoZCy4AAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBpEEwAAIBptCqYPP3005o6daoOHz5srysuLta///u/Kzk5WQ888ID+93//97r3bdmyRT/60Y9055136qmnntLFixdb33IAANDltDiY7NixQ1euXGlSV1dXp5SUFPn4+Gjz5s2aO3euUlNTmwSXHTt26O2339YTTzyhtLQ0VVdXa9WqVW2/AwAA0GW0KJiUlpbqzTff1IoVK5rUZ2dnq6ysTCtWrFBoaKjuvvtuTZs2Tenp6fZr0tPTNXv2bE2aNElhYWFasWKFjhw5osLCwva5EwAA0Ok1O5g0NDRo7dq1euihh9S/f/8mrx07dkzh4eHy9PS010VHR6ugoECSVFtbqxMnTigqKsr+elBQkAICApSfn9/WewAAAF1Es4PJ+++/Lw8PD911113XvVZeXi5fX98mdb6+vqqoqJAkWa1WNTQ0yM/P74bXAAAA9GjORadPn9bWrVu1efPmVn0Tm83WqvetXLlS7u7ukqTk5GQlJye36nMAAMDNsXPnTu3cuVOSMUPSVs0KJgUFBbp48aLuv//+JvUrVqzQ1KlTFRgYqKKioiavVVRU2EdRfHx85OrqqvLy8hte811Wr14tb2/v5jQRAAA4wbUDB1arVRs3bmzT5zUrmCQlJWnkyJFN6h555BEtXbpUcXFxOn78uLZu3aqamhp5eHhIknJycjRq1ChJkru7u4YNG6bc3FxZLBZJUklJiUpLSxUREdGmGwAAAF1Hs4JJnz591KdPn+vqAwIC1L9/f/n6+qpfv35at26d5s+fr4KCAmVkZGjt2rX2a2fNmqW0tDSNGDFCgYGB2rRpkyIjIxUWFtZ+dwMAADq1ZgWT79OzZ0+tWbNGqampWrhwofz9/bVkyRL76IgkzZgxQ+Xl5Vq/fr2qqqpksVi0fPny9vj2AACgi3DJzMxs3crUm6i6ulozZ85UZWUla0wAAOgkrFarfHx8tH37dnl5ebXqM3hWDgAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMA2CCQAAMI0ezbloy5Yt+uCDD1RWVqZevXppzJgxeuyxxxQSEiJJKi4uVmpqqvLz8+Xn56d58+ZpxowZ133Gtm3bVFVVJYvFomXLlsnf37/97wgAAHRazRoxCQoK0pNPPqnf//73evHFF+Xq6qqUlBRJUl1dnVJSUuTj46PNmzdr7ty5Sk1N1eHDh+3v37Fjh95++2098cQTSktLU3V1tVatWnVz7ggAAHRazRoxmTJlSpOvH374YS1YsEAXL15UQUGBysrK9Oqrr8rT01OhoaHKy8tTenq6LBaLJCk9PV2zZ8/WpEmTJEkrVqzQnDlzVFhYqLCwsPa9IwAA0Gm1eI3JlStX9MEHHygkJES+vr46duyYwsPD5enpab8mOjpaBQUFkqTa2lqdOHFCUVFR9teDgoIUEBCg/Pz8drgFAADQVTRrxESSsrKy9Oyzz+rKlSsaOHCg1q1bJ1dXV5WXl8vX17fJtb6+vqqoqJAkWa1WNTQ0yM/P74bXAAAASC0YMRk3bpxef/11bdiwQYMHD9Zzzz2nurq6732fzWZrUwMBAED30ewREw8PDwUHBys4OFjh4eG65557lJ2dLT8/PxUVFTW5tqKiwj6K4uPjYx9ZudE1N7Jy5Uq5u7tLkpKTk5WcnNzc5gIAgA6wc+dO7dy5U5KxfKOtmh1Mvs1ms8nNzU3h4eHaunWrampq5OHhIUnKycnRqFGjJEnu7u4aNmyYcnNz7YthS0pKVFpaqoiIiH/4PVavXi1vb+/WNhEAANxk1w4cWK1Wbdy4sU2f16xg8sorrygpKUl9+/ZVeXm53nvvPfn4+GjMmDHq1auX+vXrp3Xr1mn+/PkqKChQRkaG1q5da3//rFmzlJaWphEjRigwMFCbNm1SZGQkO3IAAEATzQomZWVleuaZZ1RZWSkfHx9FRkbqxRdfVJ8+fSRJa9asUWpqqhYuXCh/f38tWbLEPjoiSTNmzFB5ebnWr19vP2Bt+fLlN+eOAABAp+WSmZlputWp1dXVmjlzpiorK5nKAQCgk7BarfLx8dH27dvl5eXVqs/gWTkAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0CCYAAMA0ejTnonfeeUe7du1ScXGxPD09FRcXp4ULF8rX19d+TXFxsVJTU5Wfny8/Pz/NmzdPM2bMaPI5W7Zs0bZt21RVVSWLxaJly5bJ39+/XW8IAAB0Xs0aMfn8889133336ZVXXtGvf/1rnTp1Ss8++6z99bq6OqWkpMjHx0ebN2/W3LlzlZqaqsOHD9uv2bFjh95++2098cQTSktLU3V1tVatWtX+dwQAADqtZo2YrF27tsnXjz/+uB5//HFVVVWpT58+ys7OVllZmV599VV5enoqNDRUeXl5Sk9Pl8VikSSlp6dr9uzZmjRpkiRpxYoVmjNnjgoLCxUWFtbOtwUAADqjVq0xqayslLu7uzw8PCRJx44dU3h4uDw9Pe3XREdHq6CgQJJUW1urEydOKCoqyv56UFCQAgIClJ+f3+Lvf+aMtHOndP58a1oPAADMqlkjJteqra3VH/7wByUnJ8vNzU2SVF5e3mS9iST5+vqqoqJCkmS1WtXQ0CA/P78bXtMS+fnS4sVSYaE0YIA0blzTEhYm/b1pAACgE2lRMKmvr9fq1aslSYsWLWr2+2w2W8ta9T1+8APp+HHp0iXps8+k3FyjvPii8bWbmxQZ6QgqUVHSmDHS3wd4AACASTU7mDQ0NGjdunUqKirS+vXr7dM4kuTn56eioqIm11dUVNhHUXx8fOTq6qry8vIbXvNdVq5cKXd3d0lScnKykpOTm7x+yy3ShAlGaVRXZ4SWnBwjrGzdKqWkSBUVUni4EVKuDSx9+za3BwAAwLft3LlTO3fulGTMqrRVs4KJzWbT888/r/z8fL388svy9vZu8np4eLi2bt2qmpoae2DJycnRqFGjJEnu7u4aNmyYcnNz7YthS0pKVFpaqoiIiBt+39WrV1/3vb73hnpIERFGmTOnsf3S2bOOsLJvn7Rxo3TqlBQS4ggrUVFGGTRIcnFp0bcFAKBbunbgwGq1auPGjW36vGYFk9TUVGVlZWnNmjWSpIsXL0oyRkLc3NwUFxenfv36ad26dZo/f74KCgqUkZHRZDfPrFmzlJaWphEjRigwMFCbNm1SZGRkh+zIcXGRBg40yg9/6KivqHBMA+XkSOnpxvoVb++mQSUqSho50gg9AADg5nHJzMz83gUgU6dO/c769957TwEBAZKkoqIi+wFr/v7+mjt3ru6+++4m17/77rtNDlhbvnz5dx6wVl1drZkzZ6qysrLFIyZtdfmydPSoEVQaS16e1NAg3XZb07By223SNRuRAADo1qxWq3x8fLR9+3Z5eXm16jOaFUw6mjODyXepr5e+/LJpWMnJkcrLjXUr0dFGUImONkZa/sGyGQAAuqz2CCZMTjSDm5sRQMLDpZ/8xKiz2YzzVHJypE8/lT75RFq/XioulkJDm4aVqCjp7wNLAADgHyCYtJKLi7FwNiREuuceR/1XXzUdVXn7bWOXUECAI6g0hpXBg1lkCwDAtQgm7axfP+mOO4zS6NIlY51K4+jK//2/xjqWW25pGlaio6XhwyVXnvkMAOimCCYd4JZbpKQkozS6fFn6/HNHWFm/XjpyxJg2Gjeu6VRQRITUs6ezWg8AQMchmDhJ795STIxRGtXVSceOGUElJ0f6/e+lJ5+UamuNk2yvHVkZM8b4DAAAuhKCiYn06GEEjjFjpHnzjLqGBunECenwYSOwbN0q/eIXxvTQmDGOoGKxGOGF7csAgM6MYGJyrq7GupPhw6UHHjDqbDbp9GkjrBw+LG3fLq1aJX39tTHt0xhUGrcv9+nj1FsAAKDZCCadkIuLNGSIUWbPNuoaj91vHFn561+ltWul0lLj1NrGoGKxGGtXTHA8DAAA1yGYdBHXHrv/T//kqC8pcYSVTz6RUlOlc+eMERiLxRFYoqMlHx/ntR8AAIlg0uUFBkozZxql0fnzjrCyZ4+0YYNxMFxYmCOsNAYWTrEFAHQkgkk3NGCANGOGURqVlRlB5fBhKStLSkuTioqkYcOuDyt+fs5rOwCgayOYQJJ0663SnXcapdGFC46wkp0tbdpkLLptDCsxMYysAADaF8EEN9S/v5ScbJRGX33l2A107chKWJgjqMTEGGGFBbYAgJYimKBF+vW7PqxcuOAIK3v3GmtWzp41Fth+O6ywdRkA8I8QTNBm/ftfPw3UuMD20CHp44+lF180dgiNGuU48TYmRho7lkPhAAAOBBPcFN+1wPbcOUdY+eAD6bnnpIsXpdGjm4aVyEipVy/ntR0A4DwEE3SYoCCj/PCHxtc2m3TmjHTwoBFY0tOlp56Sqqqk225rGlbGjOFBhgDQHRBM4DQuLlJIiFHuvdeos9mkkyeNUZVDh6T33pOWL5euXDGO14+NNYJKbKxxoq2bm1NvAQDQzggmMBUXF2noUKP8+MdGXUOD9OWXRlA5eFB67TVp0SIjlERHO4JKTIyxldnFxbn3AABoPYIJTM/V1RgdGTlSmjPHqKurkwoKjKBy6JCxuDYvz9j10xhUGktwsHPbDwBoPoIJOqUePYx1KLfdJj3yiFF35Yr02WdGWDl4UPrP/5Ty842FuN8OK337Orf9AIDvRjBBl9Grl2Ox7KJFRl11tZST4wgrf/iDVFhoTBVdG1Q4YwUAzIFggi7Ny0tKSjJKo4sXHetVdu82poHOnzfOWImLM0psrDEa4+7uvLYDQHdEMEG34+8v/eAHRml09qxjVOXPf5Z+8Qvp8mVjJ9C1YWX4cGPNCwDg5iCYADIWyAYHS7NmGV83NBhTPgcPSgcOGA8w/PRTqXdvx/RPY2AJCnJq0wGgSyGYAN/B1VUaMcIojTuBrl51LK49cMCxuDYgwBFS4uKMNS4+Ps5tPwB0VgQToJl69jQWyUZHSwsXGnVVVcZIyoEDRnnlFeNpyyNHSvHxjrASGcl6FQBoDoIJ0AZ9+kiTJhml0fnzjlGV//N/pKefNnYHRUU1DSthYRwGBwDfRjAB2tmAAdLMmUaRjGP2Cwul7GwjsPzud8YWZi+vplNAcXHSrbc6t+0A4GwEE+Amc3ExdvMMHy49+KBRV1srHTlihJUDB6Q//Un64gtp8GDHqEp8vDFt5OHh3PYDQEcimABO4O7uOAzu3/7NqKuoMM5Xyc6WPv5YWrfOOHPlttuMkNIYWMLD2bIMoOsimAAm4esr3X67USRjCqioyDGq8sYb0s9+ZizCjY11hJX4eGP6CAC6AoIJYFIuLsbUzuDBjictX70qff65EVays42D4I4dc0wBNZboaOPMFQDobAgmQCfSs6exuycqSnrsMaOustJYVJudLWVkSGvWSOXlxqm18fHS+PHGn+wCAtAZEEyATs7H5/opoFOnpP37jbCycaO0YIGxtfnaoBIXJ/n5ObXpAHAdggnQxbi4SKGhRvnJT4y6K1ekvDxHWHnrLenkSeMguPHjHWFlzBipB/8qAHAi/gkCuoFevRxnpTS6cMEIKfv3G9uV/+M/pPp6Y2HttWElMNB57QbQ/RBMgG6qf/+mB8HV1xsLabOyjMDyn/8pHT0qhYQ4gsr48cb6FhbWArhZCCYAJElubtLo0Ub5l38x6qxWY2Ht/v1SZqa0erWx2DYqyggpCQnGn4MHs7AWQPsgmAC4IW9vafp0o0jGwtq//c0IKvv3Sy++KOXmSv36OUZUEhIki8U4ch8AWopgAqDZXFykYcOMMmeOUffNN8YTlrOyjLCyYYNUViaNHesYUUlIkIYOZVQFwPcjmABoE09PKSnJKJIxqlJcbASVrCzjoYWPPGKcbNsYUhISjEW2jKoA+DaCCYB25eIiDRpklPvvN+ouX3aMqmRlSWlp0vnzUmSkI6gwqgJAIpgA6AC9e0sTJhil0bWjKmlpjlGVa4NKbKwxIgOg+2hWMNm1a5f+8pe/6Pjx46qurtaHH34oNzc3++vFxcVKTU1Vfn6+/Pz8NG/ePM2YMaPJZ2zZskXbtm1TVVWVLBaLli1bJn9///a9GwCdRkiIURqfA9Q4qrJ/v7RvnzEFdO1alQkTjD/ZAQR0bc16ePqVK1cUHR2tnzQeI3mNuro6paSkyMfHR5s3b9bcuXOVmpqqw4cP26/ZsWOH3n77bT3xxBNKS0tTdXW1Vq1a1X53AaDTaxxVWbpUev996cwZ6cQJaflyydVVSk2Vhg+XgoKk2bOlF14wAszly85uOYD21KwRkzvuuEOSlJube91r2dnZKisr06uvvipPT0+FhoYqLy9P6enpslgskqT09HTNnj1bkyZNkiStWLFCc+bMUWFhocLCwtrpVgB0JdeuVXngAaPum2+kQ4eMQLJ7t/Tb3xrnqkRHO6aKEhKM8AKgc2rWiMk/cuzYMYWHh8vzmong6OhoFRQUSJJqa2t14sQJRUVF2V8PCgpSQECA8vPz2/rtAXQjnp7SpEnSL34h/c//GAtojx6VFi0yQsuzzxrTQ0OGSD/9qbF25dNPpbo6Z7ccQHO1efFreXm5fH19m9T5+vqqoqJCkmS1WtXQ0CC/bz3G9NprAKA1XFyksDCjzJtn1Fmt0oEDxqjK9u3SU08Zx+3HxztGVcaP58nKgFnd9F05NpvtZn8LALDz9pZuv90oktTQIOXnG0Fl3z7pySelwkJp1ChHUElMNNavsKgWcL42BxM/Pz8VFRU1qauoqLCPovj4+MjV1VXl5eU3vOZGVq5cKXd3d0lScnKykpOT29pcAN2Mq6s0ZoxR/vVfjbqyMmP3z9690n//tzEV1KePI6RMmCDFxEgeHs5tO9AZ7Ny5Uzt37pRkLN9oqzYHk/DwcG3dulU1NTXy+Pvf4pycHI0aNUqS5O7urmHDhik3N9e+GLakpESlpaWKiIj4h5+9evVqeXt7t7WJANDErbdK99xjFEmqrTXWojSOqqxfL339tbGotjGoJCZKAQFObTZgStcOHFitVm3cuLFNn9esYGK1WlVWVqazZ89KkgoLC+Xm5qbg4GDFxcWpX79+WrdunebPn6+CggJlZGRo7dq19vfPmjVLaWlpGjFihAIDA7Vp0yZFRkayIweAKbi7Ox5CuHSpcaz+yZNGSNm7V1q1Svr8cyk0tGlQGT3aGJEB0H5cMjMzv3cRyAcffKB169ZdV//SSy9p3LhxKioqsh+w5u/vr7lz5+ruu+9ucu27777b5IC15cuX3/CAterqas2cOVOVlZWMmAAwhcpKx/TP3r1SdrbUo4exPTkx0ShxcTz/B92b1WqVj4+Ptm/fLq9W/mVoVjDpaAQTAGZXVyfl5RkhpXFkpaREiopyjKgkJkrBwc5uKdBx2iOY8KwcAGiFHj0ki8UoTzxh1BUVOUZU1qyRjhwxDohLSnIEFaZ/gH+MYAIA7aTxpNrGp3dYrY7pn/ffN47X79nTMf2TlGRM/7D7B3AgmADATeLtLf3gB0aRmk7/7NkjbdwoffWVsfsnKckxstK/v3PbDTgTwQQAOsi3p39sNunUKSOk7N0rPf20cRjc8OGOEZWkJA5/Q/dCMAEAJ3FxMbYgh4ZKc+cadRcvSllZRlj5/e+ln/1M8vFxhJSkJGOBbQ/+9UYXxY82AJiIv790991GkaTLl6XDh42gkpEhPfeccSDc+PGOoDJ+vHFyLdAVEEwAwMR693bs6Pn5z41n/xQUGEFl927pzTelM2eMUZRrR1UGDHB2y4HWIZgAQCfi6mpsOR49Wlq40KgrLjaCyp49jlNqw8IcIWXiRONr1qmgMyCYAEAnFxJibFFu3KZcXm6sU9m92/GQQn9/R0iZOFGKjJTc3JzbbuC7EEwAoIvx85NmzDCKZKxTOXjQCCo7dhi7fyTjhNrGoBIXZ0wbAc5GMAGALq53b0cAkaT6euNU2t27jfK73xmjLHFxjusmTDB2AwEdjWACAN2Mm5uxWDYqynGeSmGhI6gsXmw8XXnsWEdQmTiRBbXoGAQTAOjmXFyMQ9yGD5ceecSoO3fOWEy7a5exRfmzz4zXJ00yQsqkSdLgwSyoRfsjmAAArhMUJP34x0aRjKmevXuNoPJf/yUtWCAFBDhCyqRJ0qhRBBW0HcEEAPC9/PykmTONIknV1VJ2tvTJJ9LWrdLSpZKXlyOoTJxoTAVxQi1aih8ZAECLeXlJ06YZRTJOoz182BhR+X//T/rVr4y1K4mJjhGVmBipVy/nthvmRzABALSZu7uUkGCUn//csfNn1y5jQe1LL0mXLhnH50+ebASV8eMlT09ntxxmQzABALS7a3f+PPmkMXpy7JgRVHbtkl5/XSorM0ZRJk0ywkpiouTt7eyWw9kIJgCAm87FxVgcO2qUcZS+zWZsSd61y1in8vjj0qlTRpBpHFGZONE4sRbdC8EEANDhXFykoUON8tBDRt2ZM46g8vOfS8ePS7fd5hhRmTRJuvVWpzYbHYBgAgAwhYEDpZ/+1CiSdP68I6isWiUdPSqFhxshpbEEBjq3zWh/BBMAgCkNGCDdd59RJOnrr42FtJ98Iv32t9KcOcaIy+TJ0pQpxp8hIU5tMtoBwQQA0Cn07SvNmmUUSaqoMA59++QT43k/Dz0kDRrkCClTphin06JzIZgAADolX1/p7ruNIhnbkRuDyubN0r/8ixQc7AgpU6ZIQ4ZwOq3ZEUwAAF3CLbdId95pFEmqqpKysqSPP5beeMPYDRQY2DSohIYSVMyGYAIA6JL69JHuuMMoknGMfmNQ+f3vpcceM9axNIaUKVOMNSsEFecimAAAugUvL+n2240iSd984wgqb74pLVpEUDEDggkAoFvy9JSmTzeKZASV/fuNoPLWWwQVZyGYAAAgI6hc+2DCa4NK44hKQIAjpEydymLam4FgAgDAd/iuoJKVJWVmSv/938Zi2qAgR0hp3PWDtiGYAADQDN+e+qmulvbtM0ZUXn1VevRR4/TaqVMdQYUD31qOYAIAQCt4eTXd9VNVZZyjkpkppaVJDz9sjKA0BpWpUzlCvzkIJgAAtIM+faTkZKNIktUq7dljBJXUVGnuXGn4cCOgTJtmjKj07+/UJpsSwQQAgJvA21uaMcMoknGE/q5dRlD5zW+kBx6QIiIcoymTJ0v+/k5tsikQTAAA6AC+vtI99xhFMh5K+MknUkaG9PTT0rFj0tixxmjK1KnSxIlGuOluCCYAADhB377SvfcaRZJKS42FtJmZ0pIl0smTUkyMI6gkJhoLcLs6ggkAACYQEGBM7zzwgPF1UZERUjIzpQULpPPnpfHjHVuY4+Mld3fntvlmIJgAAGBCgwZJ8+cbxWaTTpwwQkpGhrRpk7ELKCnJEVSioqQeXeC3ehe4BQAAujYXFykszCiPPmoElfx8I6RkZkpr10oNDcYC2unTjaAyZkznPJWWYAIAQCfj4iKNHm2UxYul+nopN9cIKR98IK1caWxfnjrVEVQ6y3N+XJ3dAAAA0DZubpLFIi1fLu3YIZWXS3/+s7Ed+d13jT+HDJEeeUR65x2ppMTZLb4xggkAAF2Mu7ux/uSXvzR2+pSXS6+/Lt16q7Rhg3F0fkSEMdryl78Yr5sFUzkAAHRxnp5Nj88vLzcCS0aGMe3zxRdSdLTjWUDO3JpMMAEAoJvx85P++Z+NIknnzhkh5aOPHFuTJ0xwBJXY2I7b8dPhwWTLli3atm2bqqqqZLFYtGzZMvlzBi8AAE4TFCQ9+KBRbDapsNAIKR99JL30klRX59jxM326sej2Zi2k7dA1Jjt27NDbb7+tJ554QmlpaaqurtaqVas6sgkAAOAfcHExHjb42GPSn/4kXbhgTPtMnGgsrI2PN4LMnDnS739vHATXnjo0mKSnp2v27NmaNGmSwsLCtGLFCh05ckSFhYUd2YxuZ+fOnc5uQpdAP7Yf+rL90Jftg368MVdX4/C2//gPYyvyxYvSH/8oDRtmLKgdOlQaMUJatEj6n/9ph+/X9o9ontraWp04cUJRUVH2uqCgIAUEBCg/P7+jmtEt8ReufdCP7Ye+bD/0ZfugH5uvVy9p0iTp2WelvXuNoJKaatSvWdP2z++wNSZWq1UNDQ3y8/NrUu/r66uKioqOagYAAGhH3t7SzJlGsVolH5+2fV6HBRObzdbia61W681qTrdSW1tLX7YD+rH90Jfth75sH/Rj+2jsw5b8zv+2DgsmPj4+cnV1Vfm3TnGpqKiQr69vk7qamhpJUkhISEc1r8vbuHGjs5vQJdCP7Ye+bD/0ZfugH9tPTU2N+vTp06r3dlgwcXd317Bhw5SbmyuLxSJJKikpUWlpqSIiIppc27dvX23dulUeHh5y6QwH+wMAANlsNtXU1Khv376t/owOPcdk1qxZSktL04gRIxQYGKhNmzYpMjJSYWFhTa5zdXVV//79O7JpAACgHbR2pKRRhwaTGTNmqLy8XOvXr7cfsLZ8+fKObAIAADAxl8zMzNavUAEAAGhHpnxWDsfWt9yuXbv0l7/8RcePH1d1dbU+/PBDubm52V8vLi5Wamqq8vPz5efnp3nz5mnGjBlObLE5vfPOO9q1a5eKi4vl6empuLg4LVy4sMkCbfry+23ZskUffPCBysrK1KtXL40ZM0aPPfaYfUE7fdh6Tz/9tPbu3asXXnjBvl6P/myeN998U2+99VaTusTERP3617+WRD+21PHjx7V582bl5+erZ8+eslgseuaZZyS1rS9NF0waj61PSUlRUFCQ0tLStGrVKm3YsMHZTTO1K1euKDo6WhaLRa+//nqT1+rq6pSSkqKwsDD7D1FqaqoGDBhg/4cNhs8//1z33XefRo4cqerqar388st69tlnlZqaKom+bK6goCA9+eSTCgoKUnV1td566y2lpKTonXfeoQ/bYMeOHbpy5UqTOvqzZcLDw/Wb3/zG/rW7u7sk+rGlTp8+raVLl2r27NlavHixXF1ddfr0aUlt70vTBZNrj62XpBUrVmjOnDkqLCy8bpEsHO74+7Osc3Nzr3stOztbZWVlevXVV+Xp6anQ0FDl5eUpPT2dv3Dfsnbt2iZfP/7443r88cdVVVWlPn360JfNNGXKlCZfP/zww1qwYIEuXryogoIC+rAVSktL9eabbyotLU0//vGP7fX8TLZMjx49vnMEnn5smTfeeEMTJ07Uww8/bK8bPHiwpLb3ZYc+K+f7cGz9zXHs2DGFh4fL09PTXhcdHa2CggIntqpzqKyslLu7uzw8PCTRl61x5coVffDBBwoJCZGvry992AoNDQ1au3atHnrooet2LNKfLXPixAnde++9mjt3rtavX69Lly5Joh9bor6+XgcPHlRAQICWLFmie++9V8uXL9eJEycktb0vTRVMOLb+5igvL7/uEDv69PvV1tbqD3/4g5KTk+3rdejL5svKytJdd92lu+66S/v379e6devshyzShy3z/vvvy8PDQ3fdddd1r9GfzRcREaGUlBS98MILWrRokfLy8vT000/LZrPRjy1QWVmpy5cv649//KOmTZumtWvXqn///lq2bJmqqqra3JemmsppyxG2QHuqr6/X6tWrJUmLFi1ycms6p3Hjxun111/XxYsXtXXrVj333HN6+eWXnd2sTuf06dPaunWrNm/e7OymdHpxcXH2/x46dKgGDx6sBx98UMePH3diqzqfhoYGSdLkyZN1zz33SJKWLVum++67T/v27Wvz55sqmLTk2Ho0n5+fn4qKiprU0ac31tDQoHXr1qmoqEjr16+3T+NI9GVLeHh4KDg4WMHBwQoPD9c999yj7Oxs+rCFCgoKdPHiRd1///1N6lesWKGpU6cqMDCQ/myl4OBg9enTRyUlJfxctkDj7+prHxvTo0cPBQYGqqysrM19aaqpnGuPrW90o2Pr0Xzh4eH64osv7M8gkqScnByNGjXKia0yJ5vNpueff175+fl64YUX5O3t3eR1+rL1bDab3Nzc6MMWSkpK0htvvKHXX3/dXiRp6dKlWrhwIf3ZBufPn1dVVZUCAgLoxxbo2bOnhg8frrNnz9rr6uvrVVpaqgEDBrS5L001YiI1/9h6NGW1WlVWVmb/QSksLJSbm5uCg4MVFxenfv36ad26dZo/f74KCgqUkZFx3Q4USKmpqcrKytKaNWskSRcvXpRk/D8ENzc3+rKZXnnlFSUlJalv374qLy/Xe++9Jx8fH40ZM0a9evWiD1ugT58+33nEd0BAgPr37y9fX1/6s5k2b96sxMRE9e/fXyUlJdq8ebNGjx6tESNGqL6+nn5sgR/96Ed6/vnnNW7cOIWHh2vbtm2SpAkTJsjd3b1NfWnKk1/ffffdJgesLV++nAPWvscHH3ygdevWXVf/0ksvady4cSoqKrIfduPv76+5c+fq7rvvdkJLzW3q1KnfWf/ee+8pICBAkujLZnjuued05MgRVVZWysfHR5GRkXr44Yc1cOBASfRhW02dOrXJAWv0Z/OsWrVKR44ckdVqVd++fRUbG6sFCxbYpxjox5Z5//339ac//UmXLl3SyJEj9cQTTyg0NFRS2/rSlMEEAAB0T6ZaYwIAALo3ggkAADANggkAADANggkAADANggkAADANggkAADANggkAADANggkAADANggkAADANggkAADCN/w/pRo+bf2OTSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are currently using default plot \n",
    "# so this will not change much about our plot\n",
    "plt.style.use('classic')\n",
    "\n",
    "plt.plot(month_number, interest_paid)\n",
    "plt.plot(month_number, principal_paid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a2455c110>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "plt.plot(month_number, interest_paid)\n",
    "plt.plot(month_number, principal_paid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a247e1590>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.plot(month_number, interest_paid)\n",
    "plt.plot(month_number, principal_paid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a240d6cd0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('tableau-colorblind10')\n",
    "\n",
    "plt.plot(month_number, interest_paid)\n",
    "plt.plot(month_number, principal_paid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a24ab5590>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "\n",
    "plt.plot(month_number, interest_paid)\n",
    "plt.plot(month_number, principal_paid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
